Recent Publications

Publications The latest 10 papers published or under review

Feature Attention Network: Interpretable Depression Detection from Social Media

Hoyun Song, Jinseon You, Jin-Woo Chung, and Jong C. Park
32nd Pacific Asia Conference on Language, Information and Computation (PACLIC 32), The Hong Kong Polytechnic University, Hong Kong SAR, December 1-3, 2018.

Extracting Supporting Evidence with High Precision via Bi-LSTM Network

ChaeHun Park, Wonsuk Yang, and Jong C. Park
30th Annual Conference on Human & Cognitive Language Technology, Korea University, Seoul, Korea, October 12-13, 2018

Automatic Tension Recognition from Lecture Show Transcripts

Seungwon Yoon, Wonsuk Yang, and Jong C. Park
30th Annual Conference on Human & Cognitive Language Technology, Korea University, Seoul, Korea, October 12-13, 2018

Extracting Spatial Information about Events from Text

Jin-Woo Chung
PhD Dissertation, KAIST, Feb. 2018

Detection of Non-Standard Meaning Usage with Word Embedding

Huije Lee, Hancheol Park, Wonsuk Yang, and Jong C. Park
Human-Computer Interaction Korea (HCI), Jeongseon, Korea, January 31-February 2, 2018.
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蹂 뿰援ъ뿉꽌뒗 遺꾩궛 몴긽 湲곕쾿쑝濡 뀓뒪듃뿉꽌 궗쟾긽쓽 쓽誘몃줈 궗슜릺吏 븡 뼱쐶(씠븯, 鍮꾪몴以 쓽誘 뼱쐶)瑜 깘吏븯뒗 紐⑤뜽쓣 젣븞븳떎. 뼱쐶쓽 뼱삎 룞씪븯굹 鍮꾪몴以 쓽誘몃줈 궗슜릺뒗 寃쎌슦瑜 뙋떒븯뒗 寃껋 옄룞솕맂 뀓뒪듃 遺꾩꽍 諛 삤뿭쓽 臾몄젣瑜 빐寃고븯뒗 뜲 以묒슂븳 슂냼씠떎. 蹂 뿰援ъ뿉꽌뒗 遺꾩궛 몴긽 湲곕쾿쑝濡 깮꽦맂 臾몃㎘ 諛 긽 떒뼱 踰≫꽣瑜 씠슜븯뿬, 긽 떒뼱媛 二쇱뼱吏 臾몃㎘ 궡뿉꽌 쟻빀븳吏瑜 寃利앺븯怨 긽 떒뼱媛 鍮꾪몴以 쓽誘몃줈 궗슜릺뿀뒗吏 뿬遺瑜 뙋떒븳떎. 蹂 뿰援ъ뿉꽌뒗 湲곗〈 뿰援ъ뿉꽌쓽 臾몃㎘ 踰≫꽣 깮꽦 諛⑹떇씠 吏땲뒗 臾몄젣젏쓣 빐寃고븯湲 쐞빐, 넻빀쟻씤 臾몃㎘ 젙蹂대 몴긽븯뒗 諛⑸쾿怨 臾몃㎘ 궡 떒뼱뱾쓽 媛以묒튂瑜 二쇰뒗 諛⑸쾿쓣 젣븞븳떎. 젣븞븯뒗 諛⑸쾿 듃쐞꽣 뜲씠꽣瑜 씠슜븳 떎뿕뿉꽌 湲곗〈뿉 젣븞맂 紐⑤뜽蹂대떎 뜑 넂 꽦뒫쓣 蹂댁떎.

Predicting Symptoms of Depression for Social Media Users via Linguistic Patterns

Hoyun Song, Hancheol Park, Wonsuk Yang, and Jong C. Park
Korea Software Congress (KSC), Busan, Korea, December 20-22, 2017.
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슦슱利앹 媛쒖씤쓽 씪긽 湲곕뒫 븯 諛 떎뼇븳 궗쉶쟻 臾몄젣瑜 빞湲고븷 닔 엳湲 븣臾몄뿉 議곌린 吏꾨떒씠 以묒슂븯떎. 씠윭븳 議곌린 吏꾨떒쓽 떆룄濡쒖꽌, 蹂 뿰援щ뒗 냼뀥 誘몃뵒뼱 뀓뒪듃瑜 씠슜븯뿬 궗슜옄뱾쓽 슦슱利 뿬遺瑜 삁痢≫븯뒗 紐⑤뜽쓣 젣븞븳떎. 蹂 뿰援ъ뿉꽌뒗 鍮꾩젙삎 뀓뒪듃씤 냼뀥 誘몃뵒뼱 뀓뒪듃 긽뿉꽌 湲곗〈쓽 뼱쐶 湲곕컲 紐⑤뜽씠 吏땶 븳怨꾩젏씤 뼱쐶 留ㅼ묶 臾몄젣 諛 슦슱利앹쓣 寃り퀬 엳吏 븡 궗슜옄뱾쓽 슦슱利 愿젴 뼱쐶 궗슜怨 愿젴븳 臾몄젣젏쓣 빐寃고븯湲 쐞빐, 蹂대떎 떖痢듭쟻씤 뼵뼱븰쟻 뙣꽩쓣 씠슜븳 紐⑤뜽쓣 젣떆븳떎. 蹂 뿰援ъ쓽 떎뿕쓣 넻빐 궗슜옄쓽 슦슱利 뿬遺瑜 삁痢≫븿뿉 엳뼱 뼵뼱븰쟻 뙣꽩쓣 븿猿 쟻슜븷 寃쎌슦 떒닚븳 뼱쐶 湲곕컲 紐⑤뜽뿉 鍮꾪빐 뜑슧 슚怨쇱쟻엫쓣 솗씤븷 닔 엳뿀떎.

Extraction of Gene-Environment Interaction from the Biomedical Literature

Jinseon You, Jin-Woo Chung, Wonsuk Yang, and Jong C. Park
Proceedings of the 8th International Joint Conference on Natural Language Processing (IJCNLP 2017), pp. 865874, Taipei, Taiwan, November 27밆ecember 1, 2017.
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Genetic information in the literature has been extensively looked into for the purpose of discovering the etiology of a disease. As the gene-disease relation is sensitive to external factors, their identification is important to study a disease. Environmental influences, which are usually called Gene-Environment interaction (GxE), have been considered as important factors and have extensively been researched in biology. Nevertheless, there is still a lack of systems for automatic GxE extraction from the biomedical literature due to new challenges: (1) there are no preprocessing tools and corpora for GxE, (2) expressions of GxE are often quite implicit, and (3) document-level comprehension is usually required. We propose to overcome these challenges with neural network models and show that a modified sequence-to-sequence model with a static RNN decoder produces a good performance in GxE recognition.

Inferring Implicit Event Locations from Context with Distributional Similarities

Jin-Woo Chung, Wonsuk Yang, Jinseon You, and Jong C. Park
Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI-17), pp. 979-985, Melbourne, Australia, August 19-25, 2017.
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Automatic event location extraction from text plays a crucial role in many applications such as infectious disease surveillance and natural disaster monitoring. The fundamental limitation of previous work such as SpaceEval is the limited scope of extraction, targeting only at locations that are explicitly stated in a syntactic structure. This leads to missing a lot of implicit information inferable from context in a document, which amounts to nearly 40% of the entire location information. To overcome this limitation for the first time, we present a system that infers the implicit event locations from a given document. Our system exploits distributional semantics, based on the hypothesis that if two events are described by similar expressions, it is likely that they occur in the same location. For example, if 쏛 bomb exploded causing 30 victims and 쐌any people died from terrorist attack in Boston are reported in the same document, it is highly likely that the bomb exploded in Boston. Our system shows good performance of a 0.58 F1-score, where state-of-the-art classifiers for intra-sentential spatiotemporal relations achieve around 0.60 F1-scores.

Using syntactic structure to extract prominent gene regulatory network from the literature

Wonsuk Yang
MS Thesis, KAIST, 2017.

Neural Theorem Prover with Word Embedding for Efficient Automatic Annotation

Wonsuk Yang, Hancheol Park, and Jong C. Park
Journal of KIISE, Vol. 44, No. 4, pp. 399-410, April, 2017.